The objectives of this project are to develop and apply statistical methods for the analysis of data collected across multiple phenotypic domains from collaborative cross (CC) strains and their derivatives. These include a diallel cross among the eight founder strains of the CC and a recombinant inbred intercross (RIX) population consisting of the F1 progeny of CC strains. The use of genetically reproducible but outbred RIX animals will enable the integration of data across multiple phenotypic domains in animals with natural levels of heterozygosity. Analysis of this novel cross design will require new methodology development. We will implement analysis tools in the general statistical software package R. Performanceof new methodology will be assessed and validated using simulations and in applications to the experimental data. Integrated analysis of genetic, environmental and physiological variables will provide new insights into the role of stressors on whole organism biology. Specific Aim 1: We will develop and apply statistical methods for genetic mapping analysis of the collaborative cross recombinant inbred strains and their derivatives. The objective of these analyses will be to identify genetic factors that interact with experimentally defined stressors in their effects on mean and covariance of measuredphenotypes. Specific Aim 2: We will develop and apply statistical methods to conduct an integrated analysis of collaborative cross data across multiple phenotypic domains. We will focus on the application of graphical models that capture interactions among these phenotypes using intuitive visual representations.